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A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Lung biopsy is currently the most effective procedure for cancer diagnosis. However, respiration-induced location uncertainty presents a challenge in precise lung biopsy. To reduce the medical image requirements for motion modelling, in this study, local lung motion information in the region of interest (ROI) is extracted from whole chest computed tomography (CT) and CT-fluoroscopy scans to predict the motion of potentially cancerous tissue and important vessels during the model-driven lung biopsy process.

Methods

The motion prior of the ROI was generated via a sparse linear combination of a subset of motion information from a respiratory motion repository, and a weighted sparse-based statistical model was used to preserve the local respiratory motion details. We also employed a motion prior-based registration method to improve the motion estimation accuracy in the ROI and designed adaptive variable coefficients to interactively weigh the relative influence of the prior knowledge and image intensity information during the registration process.

Results

The proposed method was applied to ten test subjects for the estimation of the respiratory motion field. The quantitative analysis resulted in a mean target registration error of 1.5 (0.8) mm and an average symmetric surface distance of 1.4 (0.6) mm.

Conclusions

The proposed method shows remarkable advantages over traditional methods in preserving local motion details and reducing the estimation error in the ROI. These results also provide a benchmark for lung respiratory motion modelling in the literature.

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (61902109), the Natural Science Foundation of Hebei Province (F2019205070 and F2017205066) and the Science Foundation of Hebei Normal University (L2019B01, L2017B06, L2019K01 and L2018K02).

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Authors and Affiliations

  1. College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China

    Dong Chen, Wei Guo, Liang Tian & Jing Liu

  2. Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang, China

    Dong Chen, Wei Guo, Liang Tian & Jing Liu

  3. Key Laboratory of Augmented Reality, School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, China

    Dong Chen, Wei Guo, Liang Tian & Jing Liu

  4. Department of Cardiothoracic Surgery, Peking Union Medical College Hospital, Beijing, China

    Hongzhi Xie

  5. Laboratory of Image Guided Surgery and Therapy (IGST), Shanghai Jiao Tong University, Shanghai, China

    Lixu Gu

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  1. Dong Chen
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  2. Hongzhi Xie
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  3. Lixu Gu
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  4. Wei Guo
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  5. Liang Tian
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Correspondence to Liang Tian or Jing Liu.

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Chen, D., Xie, H., Gu, L. et al. A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy. Int J CARS 15, 1279–1290 (2020). https://doi.org/10.1007/s11548-020-02154-7

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  • DOI: https://doi.org/10.1007/s11548-020-02154-7

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